AI, Collaboration & Credit Unions’ Next Member Leap

AI, Collaboration & Credit Unions’ Next Member Leap

Most credit unions aren’t struggling because they lack technology. They’re struggling because they’re choosing it alone.

Barb Lowman, President of CUNA Strategic Services (CSS), keeps coming back to one simple line:

“We help credit unions find solutions to their challenges.”

Inside that sentence is the real story: as AI, fraud threats, and member expectations accelerate, collaboration is now a survival skill. The credit unions that win the next decade won’t be the ones with the flashiest AI platform. They’ll be the ones that tap the right partners, share lessons, and deploy AI in ways that actually feel member-centric, not machine-centric.

This post connects what Barb and the CSS team are seeing across the system with a practical roadmap: how AI, collaborative solution providers, and the “safeguarding your credit union’s future” mindset can work together. If you’re a CEO, COO, CIO, or board member trying to make smart bets for 2025 and beyond, this is your playbook.

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Why Collaboration Is Now a Core AI Strategy

The reality: no single credit union can build, vet, and maintain every AI solution it needs. Not at the pace risk and member expectations are changing.

That’s where organizations like CUNA Strategic Services come in. CSS connects credit unions and leagues with vetted solution providers. They talk daily with leaders across asset sizes, collect patterns, then match credit unions with tools that address real pain points.

Here’s why that model matters so much for AI:

• Risk is higher. AI touches fraud, underwriting, compliance, and reputation. A bad vendor choice is no longer just a budget issue; it’s a safety-and-soundness issue.
• The market is noisy. Every fintech calls its product “AI-powered.” Most aren’t transparent about models, training data, or governance.
• Staff capacity is thin. Your team doesn’t have the bandwidth to run 12 proofs of concept just to figure out what actually works.

CSS acts like an industry-level due diligence partner. They see what’s working across hundreds of credit unions, then curate a smaller set of solutions. When you’re talking about AI for fraud detection, loan decisioning, and member service automation, that curation is gold.

What collaborative AI looks like in practice

Here’s what I’ve seen work when a credit union leans into collaborative sourcing:

1. Start with problems, not tools. “We need a chatbot” turns into “We’re missing 30% of member calls during lunch and evenings.” That’s a very different conversation with vendors.
2. Borrow questions from peers. Ask, “What did other credit unions our size learn the hard way?” before you sign with any AI provider.
3. Co-design pilots. Work with partners who will adapt workflows to your culture, not just plug in a black box.

This is exactly the gap CSS and its solution partners are trying to fill—connecting the dots between real challenges and credible AI solutions.

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Safeguarding Your Credit Union’s Future With AI

CSS’s webinar series, “Safeguarding Your Credit Union’s Future,” is built around a simple truth: future-proofing isn’t about predicting every trend. It’s about building the capabilities to respond quickly and safely.

For AI, that comes down to four big areas: fraud detection, loan decisioning, member service, and financial wellness.

1. AI fraud detection: From rearview mirror to real time

Traditional fraud tools are mostly reactive. AI-powered fraud detection flips that script by:

• Analyzing transaction patterns across millions of data points
• Spotting anomalies in real time instead of after the fact
• Adapting as fraudsters change tactics

A practical example:

• A mid-sized credit union feeds card transaction data into an AI model.
• The system learns normal patterns for each member (merchants, time of day, location).
• When a transaction hits that’s statistically unusual (say, three high-dollar international purchases in 10 minutes), it flags and pauses the transaction.

The result? Fewer false positives than rule-based systems and faster intervention on real threats.

What to watch for when evaluating AI fraud partners:

• Explainability: Can they explain why a transaction was flagged?
• Integration: Do they plug into your existing card/cores, or will IT be buried for six months?
• Collaboration: Are they already working with other credit unions and leagues, or treating you like a one-off experiment?

2. AI loan decisioning: Faster yes, smarter no

AI for loan decisioning isn’t about replacing human judgment. It’s about giving your team better, faster insight so members aren’t stuck waiting days for an answer.

Modern models can:

• Pull in alternative data (cashflow behavior, utility payment patterns, etc.)
• Score risk more accurately for thin-file or no-file members
• Reduce manual effort for clean applications so underwriters can focus on edge cases

One credit union I spoke with cut average decision time on standard consumer loans from hours to minutes after adopting an AI risk model paired with human review. Declines dropped for members who were actually creditworthy but didn’t fit traditional criteria.

Key guardrails you must insist on:

• Bias testing: Regular reports on model performance across demographics
• Human-in-the-loop: Clear thresholds where staff review is required
• Governance: A cross-functional team (lending, risk, compliance, IT) reviewing outcomes quarterly

This is exactly the kind of thing a collaborative partner like CSS encourages: not just deploying AI, but deploying it responsibly.

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Turning AI Into Member-Centric Service, Not Just Cost Savings

Here’s the thing about AI in member service: if your strategy is “replace people with bots,” you’ll damage trust faster than you reduce expenses.

Member-centric banking means something different:

Use AI to handle the repetitive stuff so humans can focus on empathy, coaching, and complex situations.

3. Member service automation that actually feels human

Modern AI assistants for credit unions can:

• Answer routine questions 24/7 (balances, card disputes, routing number)
• Route complex issues to the right human with full context
• Surface cross-sell opportunities based on behavior—not scripts

When they’re designed well and trained on your policies, these systems reduce call volume and improve satisfaction.

Signals you’re doing AI member service right:

• Average handle time drops, but CSAT or NPS doesn’t fall with it
• Members can easily reach a human when they want to
• Frontline staff say, “This makes my job easier,” not, “This is watching me.”

Again, this is where collaboration matters. CSS hears from executives across the country about what members complain about, what they love, and which vendors deliver on their promises. That field intelligence is hard to replicate alone.

4. Financial wellness tools that scale personalized guidance

The most interesting AI use case for member-centric banking right now isn’t chatbots; it’s personalized financial wellness.

AI can help your credit union:

• Analyze spending and saving patterns across segments
• Proactively flag risk (e.g., members trending toward overdrafts)
• Suggest micro-actions: “If you move $50 more into savings this month, you’ll hit your emergency fund goal in 90 days.”

A simple, realistic scenario:

• A member logs into your mobile app.
• They see a tailored insight: “You’ve paid $72 in subscription services in the last 30 days. Want help reviewing them?”
• With one tap, they get a categorized list and the option to cancel or adjust.

That’s member-centric AI. You’re not just marketing another product; you’re helping members feel in control of their money.

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From Projects to Strategy: Reimagining How You Choose AI

Barb has talked about CSS rebranding and reimagining their strategy to keep serving the credit union system. Credit unions need to do the same thing with AI: stop treating it as a string of disconnected projects and start treating it as a strategic capability.

Here’s a practical way to do that.

Step 1: Name your 3–5 critical challenges

Avoid vague goals like “be more digital.” Get painfully specific:

• “Reduce card fraud losses by 30% over 18 months.”
• “Cut average consumer loan decision time to under 15 minutes.”
• “Answer 80% of routine member questions instantly, 24/7.”

Those targets give your partners something real to work with.

Step 2: Build a cross-functional AI council

The credit unions that make smart AI bets almost always have a small internal group that owns it:

• Lending
• Member experience/operations
• IT/data
• Risk/compliance
• A senior sponsor (CEO/COO)

This group doesn’t need to be huge. It does need clear authority to evaluate vendors, approve pilots, and set guardrails.

Step 3: Use collaborative networks as your filter

Instead of reacting to every sales email, use partners like CSS and league relationships as your front door to AI vendors:

• Ask for 3–5 providers that have already been vetted
• Talk to peer credit unions already using those tools
• Start with time-boxed, narrowly scoped pilots

You get the benefit of others’ trial and error, without repeating their mistakes.

Step 4: Combine tech adoption with culture-building

Barb often emphasizes passion, commitment, and intentionality. That’s not feel-good fluff; it’s the difference between AI that sticks and AI that stalls.

Practical culture moves:

• Train staff on why you’re using AI, not just how
• Celebrate member stories where AI + humans solved a problem better together
• Encourage feedback from frontline teams and actually act on it

Intentionality and kindness sound soft, but in a member-owned financial cooperative, they’re strategic assets. They keep AI aligned with mission.

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Where Credit Unions Go From Here

Most credit union leaders I talk to aren’t afraid of AI. They’re afraid of picking the wrong thing, wasting scarce resources, or eroding member trust.

Collaborative partners like CUNA Strategic Services exist to lower that risk. They talk to executives every week, run series like “Safeguarding Your Credit Union’s Future,” and constantly re-evaluate which solution providers actually help credit unions thrive.

If you’re shaping your 2026–2027 roadmap, here are three moves worth making in the next 90 days:

1. Name your top three AI use cases across fraud, lending, service, and wellness.
2. Pull in a collaborative partner—CSS, your league, and trusted peers—to identify a short list of proven solutions.
3. Launch one focused pilot with clear success metrics and member-centric guardrails.

The future of AI for credit unions isn’t about chasing every new tool. It’s about choosing a few solutions that directly improve members’ lives, backed by partners who understand the cooperative system.

Member-centric banking has always been the credit union differentiator. AI, used well and chosen collaboratively, is how you scale that differentiator into the next decade.